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Neural Networks with R

You're reading from   Neural Networks with R Build smart systems by implementing popular deep learning models in R

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781788397872
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Balaji Venkateswaran Balaji Venkateswaran
Author Profile Icon Balaji Venkateswaran
Balaji Venkateswaran
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (8) Chapters Close

Preface 1. Neural Network and Artificial Intelligence Concepts 2. Learning Process in Neural Networks FREE CHAPTER 3. Deep Learning Using Multilayer Neural Networks 4. Perceptron Neural Network Modeling – Basic Models 5. Training and Visualizing a Neural Network in R 6. Recurrent and Convolutional Neural Networks 7. Use Cases of Neural Networks – Advanced Topics

Generalization of neural networks


Generalization is aimed at fitting the training data. It is an extension of the training we have done on the neural networks model. It seeks to minimize the sum of squared errors of the model on the training data (such as using ordinary least squares) and reduce the complexity of the model.

The methods of generalization are listed here:

  • Early stopping of training
  • Retraining neural networks with different training data
    • Using random sampling, stratified sampling, or any good mix of target data
  • Training multiple neural networks and averaging out their outputs
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